AI TRANSFORMATION ACCELERATOR

What’s your organization’s current level of maturity?

Please answer the following questions and add together the points to determine your score.

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BUSINESS

How has your organization identified where AI will create business value?*

a. We have a prioritized roadmap of AI use cases with defined links to business outcomes and KPIs
b. We’ve identified promising areas and are working to prioritize and develop business cases
c. We’re exploring AI’s potential broadly across various areas of the business

How does your organization approach AI investment decisions?*

a. AI investments are evaluated as proposals emerge from different parts of the business
b. We have a structured framework evaluating AI investments against strategic priorities with ROI expectations
c. We have general criteria for evaluating AI initiatives, with business cases developed for key projects

How well does your organization understand which processes are suited for AI?*

a. We’ve assessed select processes for AI potential and continue to expand this analysis
b. We’ve systematically mapped key processes and identified where AI addresses specific business needs
c. We have a general view of where AI could add value and are exploring specific opportunities

How does your organization connect AI initiatives to business results?*

a. Some AI initiatives have success metrics; measurement approaches are being developed across the portfolio
b. AI initiatives are evaluated qualitatively, with formal measurement frameworks under consideration
c. AI initiatives have defined success metrics with regular tracking against business KPIs

What level of leadership support exists for AI transformation?*

a. C-level accountability with dedicated multi-year budget and board mandate
b. Executive sponsor identified with discretionary budget, balanced with other priorities
c. AI is part of strategic discussions, with formal budget allocation and ownership being evaluated

How engaged is middle management in driving AI adoption?*

a. Some middle managers champion AI within their teams, with engagement growing across the organization
b. Middle managers are accountable for AI in their areas, equipped with tools and training to lead change
c. Middle managers are informed about AI initiatives and provide input as opportunities arise

How is your organization building AI-related skills and capabilities?*

a. Evaluating talent requirements and considering various approaches to build AI capabilities
b. Active talent program underway with upskilling initiatives, key hires and/or strategic partners engaged
c. Strategy defined combining hiring, training and partnerships, now in early implementation

How does your organization approach experimentation and learning?*

a. Test-and-learn culture where rapid experimentation and iteration are encouraged
b. Structured processes with established governance and stage-gate approval mechanisms
c. Growing openness to experimentation, with pilots underway in specific areas

How would you characterize your data readiness for AI?*

a. Core data partially integrated with improving quality standards; advanced AI workloads require coordination
b. Unified data platform with governance, consistent quality and access ready for AI/ML workloads
c. Data managed within departmental systems; AI projects involve dedicated data preparation work

How ready is your technology infrastructure for AI?*

a. Cloud-first, API-driven architecture with AI security protocols and scalable infrastructure
b. Hybrid environment with cloud migration underway and security framework being enhanced for AI
c. Stable infrastructure supporting current operations, with cloud and AI-specific capabilities under evaluation

How mature are your AI governance and security capabilities?*

a. Standard enterprise security in place, with AI-specific governance requirements being assessed
b. Comprehensive AI security protocols with model governance, bias monitoring and explainability frameworks
c. Security framework being enhanced for AI/ML, with model governance policies in development

How prepared is your organization to deploy and manage an AI solution architecture?*

a. Basic data science capabilities exist, with governance and MLOps in early development
b. MLOps platform operational with model versioning, monitoring, CI/CD pipelines and performance tracking
c. Core IT priorities in focus, with AI deployment managed through vendor partnerships as needed

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